一种基于元胞自动机的人群疏散仿真算法研究

以安徽博物院内人群疏散问题为例,根据最小费用最大流与广度优先搜索(BFS)方法提出了一种基于元胞自动机的人群疏散仿真算法.首先,算法通过设定数值矩阵,分别对人物状态、地形状态与影响人群逃离的因素进行量化; 其次,结合最小费用最大流的思想,建立人群在复杂地形中的逃离规则; 最后,应用该算法以疏散单层和多层人群为例进行仿真,结果显示博物院1层的疏散性能弱于2层,工作人员的疏散引导可大幅缩短疏散时间(由无人引导的4 423 s降至为1 876 s).对比3种不同算法的仿真结果显示,本文算法优于传统算法,与文献[7]算法接近,但本文算法因考虑了人群疏散时的心理因素,因此本文算法更符合实际.

Research on crowd evacuation algorithm based on cellular automata

Taking the crowd evacuation problem in Anhui Museum as an example, a cellular automata based crowd evacuation simulation algorithm is proposed based on the minimum cost and maximum flow and BFS method. Firstly, the algorithm quantifies the character state, terrain state and the factors that affect people's escape by setting a numerical matrix. Secondly, combining with the idea of the minimum cost and maximum flow, the rules of people's escape in complex terrain is established. Finally, the algorithm is used to simulate the evacuation of single -layer and multi -layer people, and the results show that the evacuation performance of the first floor of the museum is weaker than that of the second floor. Under the guidance of the staff, the evacuation time can be greatly reduced(from 4 423 s without guidance to 1 876 s). The simulation results of three different algorithms show that the algorithm in this paper is better than the traditional algorithm and close to the algorithm in literature [7], but the method in this paper is more practical because of considering the psychological factors of evacuees.